We investigate an energy function for MLP called superposed energy. Applying to autoassociative learning of a sandglass-type MLP, it can adaptively adjust the effective number of the bottleneck-layer units to the intrinsic dimensionality of nonlinear data, and the optimal dimensionality reduced representation can be extracted after learning.